Deriving target data from selected brain data

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a subset of brain data of a patient. One of the methods comprises providing connectivity data for presentation to a user, the connectivity data characterizing, for each pair of parcellations comprising a first parcellation and a second parcellation from a plurality of parcellations, a degree of correlation between the brain activity of the first parcellation and the brain activity of the second parcellation in the brain of a patient; determining one or more elements of interest in the connectivity data; determining one or more parcellations associated with elements of interest in the connectivity data; obtaining brain atlas data; determining a subset of the brain atlas data associated with the determined parcellations; and providing the subset of the brain atlas data to a user device for rendering the subset to the user.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 16/988,463, filed Aug. 7, 2020, the entire contentsof which is incorporated herein by reference.

BACKGROUND

This specification relates to processing data related to the brain of apatient, e.g., functional magnetic resonance imaging (MRI) data and/ortractography data.

Brain functional connectivity data characterizes, for each of one ormore pairs of locations within the brain of a patient, the degree towhich brain activity in the pair of locations is correlated.

One can gather data related to the brain of the patient by obtaining andprocessing images of the brain of the patient, e.g., using magneticresonance imaging (MM), diffusion tensor imaging (DTI), or functional MMimaging (fMRI). Diffusion tensor imaging uses magnetic resonance data tomeasure diffusion of water in a human brain. One can use the measureddiffusion to generate tractography data, which can include images ofneural tracts and corresponding white matter fibers of the subjectbrain.

Data related to the brain of a single patient can be highly complex andhigh-dimensional, and therefore difficult for a clinician to manuallyinspect and parse, e.g., to plan a surgery or diagnose the patient for abrain disease or mental disorder. For example, a correlation matrix,e.g., a correlation matrix of fMRI data, of the brain of a patient canbe a matrix with hundreds of thousands or millions of elements.

SUMMARY

This specification relates to a system that can determine a subset ofbrain atlas data that is associated with a selected subset of brain dataof a patient. The system can receive a user input identifying theselected subset of the brain data of the patient. The system can use thedetermined subset of the brain atlas data to generate target data fortreatment of the patient. For example, the system can generate targetdata for high-precision targeted treatment of the brain, e.g., fortranscranial magnetic stimulation (TMS). The system can also provide thesubset of the brain atlas data for display to a user.

In this specification, brain data can be any data characterizing thebrain of a patient. For example, brain data can include one or both ofi) direct measurement data of the brain of the patient, e.g., images ofthe brain collected using brain imaging techniques, or ii) data that hasbeen derived or generated from initial measurement data of the brain ofthe patient, e.g., correlation matrices.

In this specification, a parcellation is a predefined region of thebrain. For example, a parcellation can be defined by boundaries on athree-dimensional volume of the brain. A parcellation can be definedsuch that the neurons in the parcellation are functionally similaraccording to one or more criteria. For example, a set of parcellationscan be defined according to changes in cortical architecture, function,connectivity, and/or topography.

In this specification, a brain atlas is data that defines one or moreparcellations of a brain of a patient, e.g., by defining the coordinatesof the outline of the parcellation or the volume of the parcellation ina common three-dimensional coordinate system.

In this specification, target data can be any data that defines aparticular region of the brain for targeted treatment. For example,target data can identify one or more particular parcellations of thebrain of a patient, e.g., parcellations as defined by a brain atlas. Insome cases, target data can be provided to one or more medical devicesfor executing the treatment of the patient, e.g., target data can beprovided to an image guidance system for delivering treatment to thepatient.

Particular embodiments of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages.

As discussed above, a set of brain data characterizing the brain of asingle patient can often be incredibly large and complicated, and thusit can be difficult and time consuming for a user to extract usefulinformation from the set of brain data. Using techniques described inthis specification, a system can quickly determine the subset of a brainatlas that is clinically relevant to a user and provide the determinedsubset for display to the user, so that the user is not forced to searchthrough and analyze a large amount of data that is not clinicallyrelevant. Therefore, the amount of time that a user must spend todiscover the portion of the brain atlas that is useful to the user canbe drastically reduced, resulting in improved outcomes for patients,users and/or clinicians, especially when effective care requires timesensitive investigations.

As a particular example, using existing systems it might take a usermultiple hours to extract the subset of brain data of a patient requiredfor a particular treatment, and because of the extensive time andattention required, the extracted subset can be inaccurate anderror-prone. Using systems described in this specification, the processof extracting useful brain data from a corpus of brain data can be fullyautomated and executed in a matter of seconds or minutes.

In this specification, data is “clinically relevant” if the datarepresents an answer to a question that a trained clinician might ask inclinical practice treating patients, e.g., a question asked by aclinician in order to treat a patient with a specific disease. Forexample, clinically relevant data can be brain data characterizing aportion of the brain that a clinician has determined behavesanomalously.

Using some existing techniques, a user might only be able to generatetarget data that identifies a large portion of the brain of a patient,e.g., the frontal lobe. Using techniques described in thisspecification, a system can generate precise target data that can beused to execute highly personalized treatment of a patient. For example,a user can identify brain data that is anomalous, and the system candetermine from the identified brain data a precise region (e.g., asingle parcellation) in the brain of the patient that is behavinganomalously. The system can then generate target data for treating thedetermined region. Therefore, treatment can be highly targeted andpersonalized for individual patients, resulting in improved healthoutcomes for patients.

In this specification, a set of brain data is “anomalous” if the valuesof the brain data are outside a normal range of values, e.g., as definedby a data set that includes brain data corresponding to multiple otherpatients, e.g., multiple other patients not known to have anomalousbrain data.

The details of one or more embodiments of the subject matter of thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A and FIG. 1B are block diagrams that illustrate an examplecomputer system for use in processing medical images.

FIG. 2A and FIG. 2B are diagrams of an example target generation system.

FIG. 3 illustrates an example connectivity matrix and an example anomalyconnectivity matrix.

FIG. 4A illustrates an example connectivity matrix and correspondingbrain image data.

FIG. 4B illustrates an example updated connectivity matrix andcorresponding updated brain image data.

FIG. 5 is a flowchart of an example process for determining target datafrom patient brain data.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

This specification describes a system that can generate target data fortargeted treatment of the brain of a patient.

FIGS. 1A and 1B are block diagrams of a general-purpose computer system100 upon which one can practice arrangements described in thisspecification. The following description is directed primarily to acomputer server module 101. However, the description applies equally orequivalently to one or more remote terminals 168.

As seen in FIG. 1A, the computer system 100 includes: the servercomputer module 101; input devices such as a keyboard 102, a pointerdevice 103 (e.g., a mouse), a scanner 126, a camera 127, and amicrophone 180; and output devices including a printer 115, a displaydevice 114 and loudspeakers 117. An external Modulator-Demodulator(Modem) transceiver device 116 may be used by the computer server module101 for communicating to and from the remote terminal 168 over acomputer communications network 120 via a connection 121 and aconnection 170. The aforementioned communication can take place betweenthe remote terminal 168 and “the cloud” which in the present descriptioncomprises at least the one server module 101. The remote terminal 168typically has input and output devices (not shown) which are similar tothose described in regard to the server module 101.

The communications network 120 may be a wide-area network (WAN), such asthe Internet, a cellular telecommunications network, or a private WAN.Where the connection 121 is a telephone line, the modem 116 may be atraditional “dial-up” modem. Alternatively, where the connection 121 isa high capacity (e.g., cable) connection, the modem 116 may be abroadband modem. A wireless modem may also be used for wirelessconnection to the communications network 120.

The computer server module 101 typically includes at least one processorunit 105, and a memory unit 106. For example, the memory unit 106 mayhave semiconductor random access memory (RAM) and semiconductor readonly memory (ROM). The remote terminal 168 typically includes as leastone processor 169 and a memory 172. The computer server module 101 alsoincludes a number of input/output (I/O) interfaces including: anaudio-video interface 107 that couples to the video display 114,loudspeakers 117 and microphone 180; an I/O interface 113 that couplesto the keyboard 102, mouse 103, scanner 126, camera 127 and optionally ajoystick or other human interface device (not illustrated); and aninterface 108 for the external modem 116 and printer 115. In someimplementations, the modem 116 may be incorporated within the computermodule 101, for example within the interface 108. The computer module101 also has a local network interface 111, which permits coupling ofthe computer system 100 via a connection 123 to a local-areacommunications network 122, known as a Local Area Network (LAN). Asillustrated in FIG. 1A, the local communications network 122 may alsocouple to the wide network 120 via a connection 124, which wouldtypically include a so-called “firewall” device or device of similarfunctionality. The local network interface 111 may include an Ethernetcircuit card, a Bluetooth® wireless arrangement or an IEEE 802.11wireless arrangement; however, numerous other types of interfaces may bepracticed for the interface 111.

The I/O interfaces 108 and 113 may afford either or both of serial orparallel connectivity; the former may be implemented according to theUniversal Serial Bus (USB) standards and having corresponding USBconnectors (not illustrated). Storage memory devices 109 are providedand typically include a hard disk drive (HDD) 110. Other storage devicessuch as a floppy disk drive and a magnetic tape drive (not illustrated)may also be used. An optical disk drive 112 is typically provided to actas a non-volatile source of data. Portable memory devices, such opticaldisks (e.g., CD-ROM, DVD, Blu-Ray Disc™), USB-RAM, portable, externalhard drives, and floppy disks, for example, may be used as appropriatesources of data to the system 100.

The components 105 to 113 of the computer module 101 typicallycommunicate via an interconnected bus 104 and in a manner that resultsin a conventional mode of operation of the computer system 100 known tothose in the relevant art. For example, the processor 105 is coupled tothe system bus 104 using a connection 118. Likewise, the memory 106 andoptical disk drive 112 are coupled to the system bus 104 by connections119.

The techniques described in this specification may be implemented usingthe computer system 100, e.g., may be implemented as one or moresoftware application programs 133 executable within the computer system100. In some implementations, the one or more software applicationprograms 133 execute on the computer server module 101 (the remoteterminal 168 may also perform processing jointly with the computerserver module 101), and a browser 171 executes on the processor 169 inthe remote terminal, thereby enabling a user of the remote terminal 168to access the software application programs 133 executing on the server101 (which is often referred to as “the cloud”) using the browser 171.In particular, the techniques described in this specification may beeffected by instructions 131 (see FIG. 1B) in the software 133 that arecarried out within the computer system 100. The software instructions131 may be formed as one or more code modules, each for performing oneor more particular tasks. The software may also be divided into twoseparate parts, in which a first part and the corresponding code modulesperforms the described techniques and a second part and thecorresponding code modules manage a user interface between the firstpart and the user.

The software may be stored in a computer readable medium, including thestorage devices described below, for example. The software is loadedinto the computer system 100 from the computer readable medium, and thenexecuted by the computer system 100. A computer readable medium havingsuch software or computer program recorded on the computer readablemedium is a computer program product. Software modules for that executetechniques described in this specification may also be distributed usinga Web browser.

The software 133 is typically stored in the HDD 110 or the memory 106(and possibly at least to some extent in the memory 172 of the remoteterminal 168). The software is loaded into the computer system 100 froma computer readable medium, and executed by the computer system 100.Thus, for example, the software 133, which can include one or moreprograms, may be stored on an optically readable disk storage medium(e.g., CD-ROM) 125 that is read by the optical disk drive 112. Acomputer readable medium having such software or computer programrecorded on it is a computer program product.

In some instances, the application programs 133 may be supplied to theuser encoded on one or more CD-ROMs 125 and read via the correspondingdrive 112, or alternatively may be read by the user from the networks120 or 122. Still further, the software can also be loaded into thecomputer system 100 from other computer readable media. Computerreadable storage media refers to any non-transitory tangible storagemedium that provides recorded instructions and/or data to the computersystem 100 for execution and/or processing. Examples of such storagemedia include floppy disks, magnetic tape, CD-ROM, DVD, Blu-Ray™ Disc, ahard disk drive, a ROM or integrated circuit, USB memory, amagneto-optical disk, or a computer readable card such as a PCMCIA cardand the like, whether or not such devices are internal or external ofthe computer module 101. Examples of transitory or non-tangible computerreadable transmission media that may also participate in the provisionof software, application programs, instructions and/or data to thecomputer module 101 include radio or infra-red transmission channels aswell as a network connection to another computer or networked device,and the Internet or Intranets including e-mail transmissions andinformation recorded on Websites and the like.

The second part of the application programs 133 and the correspondingcode modules mentioned above may be executed to implement one or moregraphical user interfaces (GUIs) to be rendered or otherwise representedupon the display 114. For example, through manipulation of the keyboard102 and the mouse 103, a user of the computer system 100 and theapplication may manipulate the interface in a functionally adaptablemanner to provide controlling commands and/or input to the applicationsassociated with the GUI(s). Other forms of functionally adaptable userinterfaces may also be implemented, such as an audio interface utilizingspeech prompts output via the loudspeakers 117 and user voice commandsinput via the microphone 180.

FIG. 1B is a detailed schematic block diagram of the processor 105 and a“memory” 134. The memory 134 represents a logical aggregation of all thememory modules (including the HDD 109 and semiconductor memory 106) thatcan be accessed by the computer module 101 in FIG. 1A.

When the computer module 101 is initially powered up, a power-onself-test (POST) program 150 can execute. The POST program 150 can bestored in a ROM 149 of the semiconductor memory 106 of FIG. 1A. Ahardware device such as the ROM 149 storing software is sometimesreferred to as firmware. The POST program 150 examines hardware withinthe computer module 101 to ensure proper functioning and typicallychecks the processor 105, the memory 134 (109, 106), and a basicinput-output systems software (BIOS) module 151, also typically storedin the ROM 149, for correct operation. Once the POST program 150 has runsuccessfully, the BIOS 151 can activate the hard disk drive 110 of FIG.1A. Activation of the hard disk drive 110 causes a bootstrap loaderprogram 152 that is resident on the hard disk drive 110 to execute viathe processor 105. This loads an operating system 153 into the RAMmemory 106, upon which the operating system 153 commences operation. Theoperating system 153 is a system level application, executable by theprocessor 105, to fulfil various high-level functions, includingprocessor management, memory management, device management, storagemanagement, software application interface, and generic user interface.

The operating system 153 manages the memory 134 (109, 106) to ensurethat each process or application running on the computer module 101 hassufficient memory in which to execute without colliding with memoryallocated to another process. Furthermore, the different types of memoryavailable in the system 100 of FIG. 1A must be used properly so thateach process can run effectively. Accordingly, the aggregated memory 134is not intended to illustrate how particular segments of memory areallocated (unless otherwise stated), but rather to provide a generalview of the memory accessible by the computer system 100 and how such isused.

As shown in FIG. 1B, the processor 105 includes a number of functionalmodules including a control unit 139, an arithmetic logic unit (ALU)140, and a local or internal memory 148, sometimes called a cachememory. The cache memory 148 typically includes a number of storageregisters 144-146 in a register section. One or more internal busses 141functionally interconnect these functional modules. The processor 105typically also has one or more interfaces 142 for communicating withexternal devices via the system bus 104, using a connection 118. Thememory 134 is coupled to the bus 104 using a connection 119.

The application program 133 includes a sequence of instructions 131 thatmay include conditional branch and loop instructions. The program 133may also include data 132 which is used in execution of the program 133.The instructions 131 and the data 132 are stored in memory locations128, 129, 130 and 135, 136, 137, respectively. Depending upon therelative size of the instructions 131 and the memory locations 128-130,a particular instruction may be stored in a single memory location asdepicted by the instruction shown in the memory location 130.Alternately, an instruction may be segmented into a number of parts eachof which is stored in a separate memory location, as depicted by theinstruction segments shown in the memory locations 128 and 129.

In general, the processor 105 is given a set of instructions which areexecuted therein. The processor 105 waits for a subsequent input, towhich the processor 105 reacts to by executing another set ofinstructions. Each input may be provided from one or more of a number ofsources, including data generated by one or more of the input devices102, 103, data received from an external source 173, e.g., a brainimaging device 173 such such as an Mill or DTI scanner, across one ofthe networks 120, 122, data retrieved from one of the storage devices106, 109 or data retrieved from a storage medium 125 inserted into thecorresponding reader 112, all depicted in FIG. 1A. The execution of aset of the instructions may in some cases result in output of data.Execution may also involve storing data or variables to the memory 134.

Some techniques described in this specification use input variables 154,e.g., data sets characterizing the brain of a patient, which are storedin the memory 134 in corresponding memory locations 155, 156, 157. Thetechniques can produce output variables 161, which are stored in thememory 134 in corresponding memory locations 162, 163, 164.

Intermediate variables 158 may be stored in memory locations 159, 160,166 and 167.

Referring to the processor 105 of FIG. 1B, the registers 144, 145, 146,the arithmetic logic unit (ALU) 140, and the control unit 139 worktogether to perform sequences of micro-operations needed to perform“fetch, decode, and execute” cycles for every instruction in theinstruction set making up the program 133. Each fetch, decode, andexecute cycle can include i) a fetch operation, which fetches or readsan instruction 131 from a memory location 128, 129, 130; ii) a decodeoperation in which the control unit 139 determines which instruction hasbeen fetched; and iii) an execute operation in which the control unit139 and/or the ALU 140 execute the instruction.

Thereafter, a further fetch, decode, and execute cycle for the nextinstruction may be executed. Similarly, a store cycle may be performedby which the control unit 139 stores or writes a value to a memorylocation 132.

Each step or sub-process in the techniques described in thisspecification may be associated with one or more segments of the program133 and is performed by the register section 144, 145, 146, the ALU 140,and the control unit 139 in the processor 105 working together toperform the fetch, decode, and execute cycles for every instruction inthe instruction set for the noted segments of the program 133. Althougha cloud-based platform has been described for practicing the techniquesdescribed in this specification, other platform configurations can alsobe used. Furthermore, other hardware/software configurations anddistributions can also be used for practicing the techniques describedin this specification.

FIGS. 2A and 2B are diagrams of example target generation systems 200and 201, respectively. The target generation systems 200 and 201 areexamples of systems implemented as computer programs on one or morecomputers in one or more locations, in which the systems, components,and techniques described below can be implemented. As depicted in FIG.2A, the target generation system 200 is configured to determine a subsetof brain atlas data according to a user selection of one or moreparticular elements of patent correlation data. As depicted in FIG. 2B,the target generation system 200 is configured to determine a subset ofbrain atlas data according to a user selection of one or more particularelements of patient tractography data.

Referring to FIG. 2A, the target generation system 200 is configured toobtain patient brain data 202 characterizing the brain of a patient andprocess the patient brain data 202 to generate target data 244. Forexample, the patient brain data 202 can include one or more ofblood-oxygen-level-dependent imaging data, fMRI data, or EEG datacaptured from the brain of the patient.

The target generation system 200 includes a brain atlas data store 210,a pre-processing system 220, a parcellation correlation system 230, atarget generation system 240, and a graphical user interface 250.

In some implementations, the target generation system 200 is local tothe user, e.g., a component of a user device. In some otherimplementations, one or more components of the target generation system200 are non-local to the user, e.g., hosted within a data center, whichcan be a distributed computing system having hundreds or thousands ofcomputers in one or more locations. As a particular example, thegraphical user interface 250 might be local to the user, while each ofthe other components of the target generation system 200 are on thecloud and communicatively connected to the graphical user interface 250.

The brain atlas data store 210 is configured to maintain brain atlasdata 212, which defines multiple parcellations of the human brain. Forexample, the brain atlas data 212 can include a model of the braincomposed of three-dimensional voxels, where each voxel is defined by alocation and orientation in a common coordinate system of the brain.Each voxel can be assigned to a particular parcellation, so that thelocation and shape of each parcellation is defined by the respectivevoxels assigned to the parcellation. As a particular example, the brainatlas data 212 can be stored using a tabular format that maps (x,y,z)locations within the brain to an identification of a particularparcellation. As another particular example, the brain atlas data 212can be stored in a document-oriented database, e.g., using a JSON model.As another particular example, the brain atlas data 212 can be storedusing a collection of three-dimensional tensors that identify, forrespective (x,y,z) locations within the brain, a particularparcellation.

The pre-processing system 220 is configured to obtain the patient braindata 202 and process the patient brain data 202 to generate patientparcellation data 222 which organizes the patient brain data 202according to multiple different parcellations of the brain of thepatient. The pre-processing system 220 can generate the patientparcellation data 222 according to the brain atlas data 212, obtainedfrom the brain atlas data store 210.

For example, the patient brain data 202 can include multiple differenttime series characterizing the activity of a respective different regionof the brain of the patient over time, e.g., a respective time seriescorresponding to each three-dimensional voxel of the brain that can bemeasured by an MM machine. The pre-processing system 220 can organizethe different time series signals by parcellation according to a brainatlas of the brain. For example, for each voxel in the brain data 202,the pre-processing system 220 can determine the parcellation to whichthe corresponding voxel in the brain atlas data 212 is assigned. Thepre-processing system 220 can then, for each parcellation, combine thedifferent time series signals corresponding to the parcellation, e.g.,by determining an average of the different time series signals.

In some implementations the pre-processing system 220 performs one ormore additional pre-processing steps to generate the patientparcellation data 222. For example, the pre-processing system 220 canperform smoothing on the patient brain data 202, e.g., to removecomponents of the patient brain data 202 that are not clinicallyrelevant such as brain activity data related to the heartbeat orbreathing of the patient. As another example, the pre-processing system220 can perform skull stripping on the patient brain data 202. Asanother example, the pre-processing system 220 can remove one or moreslices in brain data 202 in order to allow for signal stabilization. Asanother example, the pre-processing system 220 can perform slice timingcorrection on the brain data 202. As another example, the pre-processingsystem 220 can perform motion correction on the brain data 202. Asanother example, the pre-processing system 220 can perform gradientdistortion correction on the brain data 202. As another example, thepre-processing system 220 can perform global intensity normalization onthe brain data 202. As another example, the pre-processing system 220can calculate one or more confounds using the brain data 202. As anotherexample, the pre-processing system 220 can apply a whitening transformto the brain data 202.

The parcellation correlation system 230 is configured to obtain thepatient parcellation data 222 and to process the patient parcellationdata 222 to generate patient correlation data 232 that characterizes,for each pair of parcellations of the multiple parcellations in thepatient parcellation data 222, a correlation between brain activity ofthe first parcellation and brain activity of the second parcellation inthe brain of the patient.

For example, the patient parcellation data 222 can include one or moretime series signals corresponding to each parcellation, and theparcellation correlation system 230 can determine, for each pair ofparcellations, a correlation between the values of the time series ofthe first parcellation and the time series of the second parcellation.

In some implementations, the parcellation correlation system 230processes the patient parcellation data to generate a correlation matrixfor display to the user. A correlation matrix can be generated from thebrain functional connectivity data of a patient, and displays thecorrelation between areas of the brain of the patient. In particular,for an element of a correlation matrix in a row that corresponds to afirst parcellation of the brain and in a column that corresponds to asecond parcellation of the brain, the value of the element characterizesthe correlation between the first parcellation and the secondparcellation.

In some implementations, the parcellation correlation system 230determines one or more pairs of parcellations whose correlation isanomalous. For example, the parcellation correlation system 230 canobtain normal correlation data that identifies, for each pair ofparcellations in the patient parcellation data 222, a range of valuesfor the correlation between the pair of parcellations that is considered“normal.” The normal range can be determined according to thecorrelation between the pair of parcellations measured in the respectivebrain of multiple other patients. For example, the normal correlationdata can be determined from brain data captured from hundreds,thousands, or millions of other patients.

As a particular example, the normal correlation data might identify, foreach pair of parcellations, an average correlation between the pair ofparcellations and a standard deviation of correlations between the pairof parcellations, as determined from the correlations measured in thebrains of the other patients. Using the normal correlation data, theparcellation correlation system 230 can determine one or more pairs ofparcellations in the patient parcellation data 222 whose correlation isanomalous, and identify the one or more determined pairs ofparcellations in the patient correlation data 232.

In some implementations, the parcellation correlation system 230generates an anomaly correlation matrix for display to the user. In thisspecification, an anomaly correlation matrix is a correlation matrixthat visually identifies one or more pairs of parcellations,corresponding to respective elements in the anomaly correlation matrix,whose correlation has been determined to be anomalous. Correlationmatrices and anomaly correlation matrices are discussed in more detailbelow with respect to FIG. 3.

The graphical user interface 250 is configured to obtain the patientcorrelation data 232 and display data characterizing the patientcorrelation data 232 to the user. For example, the graphical userinterface 250 can display a list of the correlation between each pair ofparcellations. As another example, the graphical user interface 250 candisplay a list of one or more pairs of parcellations whose correlationthe parcellation correlation system 230 has determined to be anomalous.As another example, the graphical user interface 250 can display acorrelation matrix or anomaly correlation matrix characterizing thecorrelation between respective pairs of parcellations in the brain ofthe patient. As another example, the graphical user interface 250 candisplay a text summary of the pairs of parcellations whose correlationthe parcellation correlation system 230 has determined to be anomalous.As another example, the graphical user interface 250 can display a scorecalculated by the parcellation correlation system 230 that characterizesa degree of anomaly in the brain of the patient, e.g., a value between 0and 1.

In some implementations, the graphical user interface 250 can alsodisplay one or more components of the patient brain data 202 to theuser. For example, the graphical user interface 250 might display brainimage data, e.g, MM images of the brain of the user.

The graphical user interface 250 is configured to receive a user inputidentifying one or more particular elements 252 of the patientcorrelation data. The selected elements 252 can include any subset ofthe patient correlation data 232. For example, the selected elements 252of the patient correlation data can include a particular pair ofparcellations, a particular parcellation, a group of pairs ofparcellations, and/or a group of parcellations (e.g., each parcellationassociated with the language region of the brain). The user can selectthe one or more particular elements 252 using any appropriate user inputdevice, e.g., the pointer device 103, or the microphone 180 depicted inFIG. 1A. As a particular example, the user can select a particular rowor column of the correlation matrix or the anomaly correlation matrix.This process is discussed in more detail below with respect to FIG. 4Aand FIG. 4B.

The graphical user interface 250 can provide the selected elements 252to the target generation system 240. The target generation system 240 isconfigured to process the selected elements 252 to determine a set ofone or more particular parcellations associated with the selectedelements 252. For example, if the selected elements 252 identify one ormore pairs of parcellations, the target generation system 240 candetermine the set of parcellations to include each parcellation includedin the one or more pairs of parcellations. The target generation system240 can obtain the brain atlas data 212 from the brain atlas data store210, and use the brain atlas data 212 to generate a subset 242 of thebrain atlas data that corresponds to the determined set ofparcellations. That is, the determined subset 242 of the brain atlasdata characterizes the location and shape of the parcellations in thedetermined set of parcellations. For example, the subset 242 of brainatlas data can identify each three-dimensional voxel in the brain atlasdata 212 that is assigned to a parcellation in the determined set ofparcellations.

The graphical user interface 250 can receive the subset 242 of the brainatlas data and render the subset 242 for the user. For example, thegraphical user interface 250 can display a three-dimensional model ofthe parcellations characterized by the subset 242 of the brain atlasdata. That is, the graphical user interface 250 can display to the usera model of the parcellations corresponding to the elements 252 that wereselected by the user. This process is discussed in more detail belowwith respect to FIG. 4A and FIG. 4B.

The graphical user interface 250 can receive a user input characterizingan export command 254 to export the subset 242 of the brain atlas data.The user can provide the export command 254 using any appropriate userinput device, e.g., the pointer device 103, or the microphone 180depicted in FIG. 1A. The graphical user interface 250 can then providethe export command 254 to the target generation system 240.

In response to receiving the export command 254, the target generationsystem 240 can generate target data 244 that characterizes the subset242 of the brain atlas data. The target generation system 240 can thenexport the target data 244 to an external system. For example, thetarget generation system 240 can provide the target data 244 to anexternal device for delivering a treatment to the patient, e.g., animage guidance system for delivering a TMS treatment to the patient. Asanother example, the target generation system 240 can provide the targetdata 244 to an external guidance device to be used during targetedsurgery of the patient. As another example, the target generation system240 can provide the target data 244 to an external user system forfurther analysis by the user. As another example, the target generationsystem 240 can provide the target data 244 to an external machinelearning system to be used as an input to one or more machine learningmodels.

In some implementations, the target generation system 240 generates andexports the target data 244 when the target generation system 240receives the selected elements 252. That is, the export command 254 canbe included in the data sent to the target generation system 240characterizing the selected elements 252, so that the target generationsystem 240 does not send the subset 242 of the brain atlas data 242 tothe graphical user interface 250 for display to the user, but ratherimmediately generates and exports the target data 244.

Referring to FIG. 2B, the target generation system 201 is configured toobtain patient brain data 202 characterizing the brain of a patient andprocess the patient brain data 202 to generate target data 274. Forexample, the patient brain data 202 can include one or more ofblood-oxygen-level-dependent imaging data, NMI data, or EEG datacaptured from the brain of the patient.

The target generation system 200 includes a brain atlas data store 210,a pre-processing system 220, a brain tractography system 260, a targetgeneration system 270, and a graphical user interface 250.

As described above with reference to FIG. 2A, in some implementations,the target generation system 201 is local to the user, e.g., a componentof a user device. In some other implementations, one or more componentsof the target generation system 200 are non-local to the user, e.g.,hosted within a data center, which can be a distributed computing systemhaving hundreds or thousands of computers in one or more locations.

The brain atlas data store 210 is configured to maintain brain atlasdata 212, which defines multiple parcellations of the human brain.

The pre-processing system 220 is configured to obtain the patient braindata 202 and process the patient brain data 202 to generate patientparcellation data 222 which organizes the patient brain data 202according to multiple different parcellations of the brain of thepatient. The pre-processing system 220 can generate the patientparcellation data 222 according to the brain atlas data 212, obtainedfrom the brain atlas data store 210. As described above with referenceto FIG. 2A, in some implementations the pre-processing system 220performs one or more additional pre-processing steps to generate thepatient parcellation data 222.

The brain tractography system 260 is configured to obtain the patientparcellation data 222 and to process the patient parcellation data 222to generate patient tractography data 262 that characterizes, for eachpair of parcellations of multiple parcellations in the patientparcellation data 222, neural tracts connecting the pair ofparcellations in the brain of the patient.

In some implementations, the brain tractography system 260 determinesone or more pairs of parcellations for which the number of connectionsin the patient tractography data 262 is anomalous. For example, thebrain tractography system 260 can obtain normal tractography data thatidentifies, for each pair of parcellations in the patient parcellationdata 222, a range of values for the number of tracts connecting the pairof parcellations that is considered “normal.” The normal range can bedetermined according to the number of tracts connecting the pair ofparcellations measured in the respective brains of multiple otherpatients. For example, the normal tractography data can be determinedfrom brain data captured from hundreds, thousands, or millions of otherpatients. As a particular example, the normal tractography data mightidentify, for each pair of parcellations, an average number of tractsbetween the pair of parcellations and a standard deviation of the numberof tracts between the pair of parcellations, as determined from theneural tracts measured in the brains of the other patients. Using thenormal tractography data, the brain tractography system 260 candetermine one or more pairs of parcellations in the patient parcellationdata 222 for which the number of tracts connecting the pair ofparcellations is anomalous, and identify the one or more determinedpairs of parcellations in the patient tractography data 262.

The graphical user interface 250 is configured to obtain the patienttractography data 262 and display data characterizing the patienttractography data 262 to the user. For example, the graphical userinterface 250 can display a list of the number of tracts between eachpair of parcellations. As another example, the graphical user interface250 can display a list of one or more pairs of parcellations whosenumber of connecting tracts the brain tractography system 260 hasdetermined to be anomalous. As another example, the graphical userinterface 250 can display a matrix characterizing the number of tractsconnecting respective pairs of parcellations in the brain of thepatient. As another example, the graphical user interface 250 candisplay a text summary of the pairs of parcellations whose number ofconnecting tracts the brain tractography system 260 has determined to beanomalous. As another example, the graphical user interface 250 candisplay a score calculated by the tractography system 260 thatcharacterizes a degree of anomaly in the brain of the patient, e.g., avalue between 0 and 1.

In some implementations, the graphical user interface 250 can alsodisplay one or more components of the patient brain data 202 to theuser. For example, the graphical user interface 250 might display brainimage data, e.g, MRI images of the brain of the user.

The graphical user interface 250 is configured to receive a user inputidentifying one or more particular elements 256 of the patienttractography data 262. The selected elements 256 can include any subsetof the patient tractography data 262. For example, the selected elements256 of the patient tractography data 262 can include a particular pairof parcellations, a particular parcellation, a group of pairs ofparcellations, and/or a group of parcellations (e.g., each parcellationassociated with the language region of the brain). The graphical userinterface 250 can provide the selected elements 256 to the targetgeneration system 270. The target generation system 270 is configured toprocess the selected elements 256 to determine a set of one or moreparticular parcellations associated with the selected elements 256. Forexample, if the selected elements 256 identify one or more pairs ofparcellations, the target generation system 270 can determine the set ofparcellations to include each parcellation included in the one or morepairs of parcellations.

The target generation system 270 can obtain the brain atlas data 212from the brain atlas data store 210, and use the brain atlas data 212 togenerate a subset 272 of the brain atlas data that corresponds to thedetermined set of parcellations. That is, the determined subset 272 ofthe brain atlas data characterizes the location and shape of theparcellations in the determined set of parcellations. For example, thesubset 272 of brain atlas data can identify each three-dimensional voxelin the brain atlas data 212 that is assigned to a parcellation in thedetermined set of parcellations.

The graphical user interface 250 can receive the subset 272 of the brainatlas data and render the subset 272 for the user. For example, thegraphical user interface 250 can display a three-dimensional model ofthe parcellations characterized by the subset 272 of the brain atlasdata. That is, the graphical user interface 250 can display to the usera model of the parcellations corresponding to the elements 256 that wereselected by the user. This process is discussed in more detail belowwith respect to FIG. 4A and FIG. 4B.

The graphical user interface 250 can receive a user input characterizingan export command 258 to export the subset 272 of the brain atlas data.The graphical user interface 250 can then provide the export command 258to the target generation system 270.

In response to receiving the export command 258, the target generationsystem 270 can generate target data 274 that characterizes the subset272 of the brain atlas data. The target generation system 270 can thenexport the target data 274 to an external system, as described abovewith reference to FIG. 2A.

In some implementations, the target generation system 270 generates andexports the target data 274 when the target generation system 270receives the selected elements 256. That is, the export command 258 canbe included in the data sent to the target generation system 270characterizing the selected elements 256, so that the target generationsystem 270 does not send the subset 272 of the brain atlas data 272 tothe graphical user interface 250 for display to the user, but ratherimmediately generates and exports the target data 274.

FIG. 3 is an illustration of an example connectivity matrix 310 and anexample anomaly connectivity matrix 320.

The connectivity matrix 310 identifies, for each pair of parcellationsof multiple parcellations in the brain of a patient, the correlationbetween brain activity of the first parcellation of the pair ofparcellations and the brain activity of the second parcellation of thepair of parcellations. That is, each row and column of the connectivitymatrix 310 corresponds to a parcellation, and each element has a valueidentifying the correlation between the parcellation corresponding tothe row of the element and the parcellation corresponding to the columnof the element. In some implementations, the connectivity matrix 310 caninclude ranges of two different colors, where the first colorcorresponds to negative correlations and the second color corresponds topositive correlations, and the intensity of a color corresponds to amagnitude of the negative or positive correlation.

The anomaly connectivity matrix 320 visually identifies one or morepairs of parcellations whose correlation is anomalous. In particular,the anomaly matrix 320 visually identifies elements that correspond topairs of parcellations whose correlations have been determined to be toovariable (in black), elements that correspond to pairs of parcellationswhose correlations have been determined to be “normal” (in white), andelements that correspond to pairs of parcellations whose correlationshave been determined to be anomalous (in grayscale).

Anomaly correlation matrices are discussed in more detail in U.S. patentapplication Ser. No. 16/920,078, the entire contents of which are herebyincorporated by reference.

FIG. 4A illustrates an example connectivity matrix 410 and correspondingbrain image data 420. Although a standard connectivity matrix isdepicted in FIG. 4A, it is to be understood that the followingdescription can also apply to anomaly connectivity matrices.

The connectivity matrix 410 and brain image data 420 can be displayed toa user by a graphical user interface, e.g., the graphical user interface250 depicted in FIG. 2. The graphical user interface can allow the userto select one or more rows or columns of the connectivity matrix 410.Because the user has not yet selected a row or column, the entire brainof the patient can be depicted in the brain image data 420.

FIG. 4B illustrates an example updated connectivity matrix 430 andcorresponding updated brain image data 440. The updated connectivitymatrix 430 is an updated version of the connectivity matrix 410, afterthe user has selected a particular column of the connectivity matrix 410(in this case, the second column). That is, after the user selects thesecond column of the connectivity matrix 410, the graphical userinterface can visually identify the selected column, displaying theupdated connectivity matrix 430. The graphical user interface canvisually identify the selected column in any way, e.g, by outlining theselected column (as depicted in FIG. 4B), highlighting the selectedcolumn, dimming the unselected columns, etc.

Although a column of the connectivity matrix 410 is selected in FIG. 4B,it is to be understood that the following description can also apply torows of the connectivity matrix 410. Similarly, although a single columnof the connectivity matrix 410 is selected in FIG. 4B, it is to beunderstood that the following description can also apply to any numberof selected columns of the connectivity matrix 410.

Each column of the updated connectivity matrix 430 corresponds to aparticular parcellation of the brain of the patient, and can have acorresponding parcellation identification IDN. The parcellationidentifications, depicted in FIG. 4B, may or may not be displayed forthe user by the graphical user interface. The user can select thedesired column in any appropriate way, e.g., by clicking on thecorresponding parcellation identification number, or by clicking on anyelement of the column.

The graphical user interface can determine the parcellationidentification of the column selected by the user (in this case, ID₂)and send the parcellation identification to a target generation system,e.g., the target generation system 240 depicted in FIG. 2. The targetgeneration system can also obtain brain atlas data defining theparcellations of the human brain. The target generation system can thendetermine, according to the parcellation identification, a subset of thebrain atlas data that characterizes the parcellation corresponding tothe selected column of the updated connectivity matrix 430. The targetgeneration system can provide the subset of the brain atlas data to thegraphical user interface.

The graphical user interface can use the subset of the brain atlas datato generate the updated brain image data 440. The updated brain imagedata 440 is an updated version of the brain image data 420, after theuser has selected the second column of the connectivity matrix 410. Theupdated brain image data 440 depicts the portion of the brain image data410 that corresponds to the parcellation corresponding to the column ofthe connectivity matrix 410 selected by the user.

In particular, the updated brain image data 440 can visually identifythe parcellation corresponding to the selected column, as defined by theobtained subset of the brain atlas data. For example, the brain atlasdata can include multiple voxels each assigned to a particularparcellation. In this case, the updated brain image data 440 can includea depiction of the voxels 450 in the brain atlas data that are assignedto the parcellation corresponding to the selected column. The locationof the depiction of the voxels 450 in the updated brain image data 440is defined by the obtained subset of the brain atlas data.

In some implementations, the user can select multiple different columnsof the connectivity matrix 410, causing the graphical user interface tovisually identify the multiple selected columns and display an updatedconnectivity matrix. The graphical user interface can also generateupdated brain image data that depicts the different portions of thebrain image data 410 that correspond to the respective parcellationscorresponding to the multiple different columns of the connectivitymatrix 410 selected by the user. In some such implementations, thegraphical user interface can visually identify the differentparcellations, e.g., using different colors where each color correspondsto a respective parcellation.

FIG. 5 is a flowchart of an example process 500 for determining targetdata from patient brain data. The process 500 can be implemented by oneor more computer programs installed on one or more computers andprogrammed in accordance with this specification. For example, theprocess 500 can be performed by the computer server module depicted inFIG. 1A. For convenience, the process 500 will be described as beingperformed by a system of one or more computers.

The system provides patient brain data for presentation to a user (step501). The patient brain data can include tractography data and/orconnectivity data characterizing the brain of a patient.

The connectivity data can characterize, for each pair of parcellationsincluding a first parcellation and a second parcellation from a set ofmultiple parcellations, a degree of correlation between the brainactivity of the first parcellation and the brain activity of the secondparcellation in the brain of a patient. In some implementations, theconnectivity data specifies a connectivity matrix and each position inthe connectivity matrix characterizes a pair of parcellations.

In some implementations, the system can identify one or more pairs ofparcellations from the set of parcellations for which the correlationbetween brain activity of the first parcellation and the secondparcellation in the brain of the patient specified in the connectivitydata is outside of a normal range of correlations. The system can thenprovide data characterizing the one or more identified pairs ofparcellations for display to the user on a graphical interface.

The system determines one or more elements of interest in the patientbrain data (step 502). For example, the system can receive a selectionfrom the user of one or more positions in a connectivity matrixcharacterizing the correlation between pairs of parcellations in thebrain of the patient. As a particular example, the system can receive aselection of a column or a row of a connectivity matrix.

The system determines one or more parcellations associated with theelements of interest (step 504). For example, each element of interestcan include an identification of a particular parcellation.

The system obtains brain atlas data (step 506).

The system determines a subset of the brain atlas data associated withthe determined parcellations (step 508).

The system provides the subset of the brain atlas data to a user devicefor rendering the subset to the user (step 510).

The system processes the determined pair of parcellations to generatetarget data for a treatment of the patient (step 512).

The system provides the target data to one or more medical devices forexecuting the treatment of the patient (step 514).

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Embodiments of the subject matter described in thisspecification can be implemented as one or more computer programs, i.e.,one or more modules of computer program instructions encoded on atangible non-transitory storage medium for execution by, or to controlthe operation of, data processing apparatus. The computer storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofone or more of them. Alternatively or in addition, the programinstructions can be encoded on an artificially-generated propagatedsignal, e.g., a machine-generated electrical, optical, orelectromagnetic signal, that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus.

The term “data processing apparatus” refers to data processing hardwareand encompasses all kinds of apparatus, devices, and machines forprocessing data, including by way of example a programmable processor, acomputer, or multiple processors or computers. The apparatus can alsobe, or further include, special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application-specificintegrated circuit). The apparatus can optionally include, in additionto hardware, code that creates an execution environment for computerprograms, e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, or acombination of one or more of them.

A computer program which may also be referred to or described as aprogram, software, a software application, an app, a module, a softwaremodule, a script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A program may, but neednot, correspond to a file in a file system. A program can be stored in aportion of a file that holds other programs or data, e.g., one or morescripts stored in a markup language document, in a single file dedicatedto the program in question, or in multiple coordinated files, e.g.,files that store one or more modules, sub-programs, or portions of code.A computer program can be deployed to be executed on one computer or onmultiple computers that are located at one site or distributed acrossmultiple sites and interconnected by a data communication network.

For a system of one or more computers to be configured to performparticular operations or actions means that the system has installed onit software, firmware, hardware, or a combination of them that inoperation cause the system to perform the operations or actions. For oneor more computer programs to be configured to perform particularoperations or actions means that the one or more programs includeinstructions that, when executed by data processing apparatus, cause theapparatus to perform the operations or actions.

As used in this specification, an “engine,” or “software engine,” refersto a software implemented input/output system that provides an outputthat is different from the input. An engine can be an encoded block offunctionality, such as a library, a platform, a software development kit(“SDK”), or an object. Each engine can be implemented on any appropriatetype of computing device, e.g., servers, mobile phones, tabletcomputers, notebook computers, music players, e-book readers, laptop ordesktop computers, PDAs, smart phones, or other stationary or portabledevices, that includes one or more processors and computer readablemedia. Additionally, two or more of the engines may be implemented onthe same computing device, or on different computing devices.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby special purpose logic circuitry, e.g., an FPGA or an ASIC, or by acombination of special purpose logic circuitry and one or moreprogrammed computers.

Computers suitable for the execution of a computer program can be basedon general or special purpose microprocessors or both, or any other kindof central processing unit. Generally, a central processing unit willreceive instructions and data from a read-only memory or a random accessmemory or both. The essential elements of a computer are a centralprocessing unit for performing or executing instructions and one or morememory devices for storing instructions and data. The central processingunit and the memory can be supplemented by, or incorporated in, specialpurpose logic circuitry. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto-optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio or video player, a game console, a Global PositioningSystem (GPS) receiver, or a portable storage device, e.g., a universalserial bus (USB) flash drive, to name just a few.

Computer-readable media suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto-optical disks;

and CD-ROM and DVD-ROM disks.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and pointing device, e.g, a mouse, trackball, or a presencesensitive display or other surface by which the user can provide inputto the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback, e.g., visual feedback,auditory feedback, or tactile feedback; and input from the user can bereceived in any form, including acoustic, speech, or tactile input. Inaddition, a computer can interact with a user by sending documents toand receiving documents from a device that is used by the user; forexample, by sending web pages to a web browser on a user's device inresponse to requests received from the web browser. Also, a computer caninteract with a user by sending text messages or other forms of messageto a personal device, e.g., a smartphone, running a messagingapplication, and receiving responsive messages from the user in return.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back-end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface, a web browser, or anapp through which a user can interact with an implementation of thesubject matter described in this specification, or any combination ofone or more such back-end, middleware, or front-end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (LAN) and a widearea network (WAN), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data, e.g., an HTML page, to a userdevice, e.g., for purposes of displaying data to and receiving userinput from a user interacting with the device, which acts as a client.Data generated at the user device, e.g., a result of the userinteraction, can be received at the server from the device.

In addition to the embodiments described above, the followingembodiments are also innovative:

Embodiment 1 is a method comprising:

-   -   providing connectivity data for presentation to a user, the        connectivity data characterizing, for each pair of parcellations        comprising a first parcellation and a second parcellation from a        plurality of parcellations, a degree of correlation between the        brain activity of the first parcellation and the brain activity        of the second parcellation in the brain of a patient;

determining one or more elements of interest in the connectivity data;

determining one or more parcellations associated with elements ofinterest in the connectivity data;

obtaining brain atlas data;

determining a subset of the brain atlas data associated with thedetermined parcellations; and

providing the subset of the brain atlas data to a user device forrendering the subset to the user.

Embodiment 2 is the method of embodiment 1, wherein providingconnectivity data for presentation to a user further comprises:

identifying one or more pairs of parcellations from the plurality ofparcellations for which the correlation between brain activity of thefirst parcellation and the second parcellation in the brain of thepatient specified in the connectivity data is outside of a normal rangeof correlations corresponding to the pair of parcellations; andproviding data characterizing the one or more identified pairs ofparcellations for display to a user on a graphical interface.

Embodiment 3 is the method of any one of embodiments 1 or 2, wherein theconnectivity data specifies a connectivity matrix and each position inthe connectivity matrix characterizes a pair of parcellations.

Embodiment 4 is the method of embodiment 3, wherein determining one ormore elements of interest in the connectivity matrix comprises receivinga selection from a user of one or more positions in the connectivitymatrix.

Embodiment 5 is the method of embodiment 4, wherein receiving aselection of one or more positions in the connectivity matrix comprisesreceiving one or more of: a selection of a column of the connectivitymatrix or a selection of a row of the connectivity matrix.

Embodiment 6 is the method of any one of embodiments 1-5, furthercomprising processing the determined pairs of parcellations to generatetarget data for a treatment of the patient.

Embodiment 7 is the method of embodiment 6, further comprising providingthe target data to one or more medical devices for executing thetreatment of the patient.

Embodiment 8 is a system comprising one or more computers and one ormore storage devices storing instructions that are operable, whenexecuted by the one or more computers, to cause the one or morecomputers to perform the method of any one of embodiments 1-7.

Embodiment 9 is a computer storage medium encoded with a computerprogram, the program comprising instructions that are operable, whenexecuted by data processing apparatus, to cause the data processingapparatus to perform the method of any one of embodiments 1-7.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or on the scope of what may be claimed, but rather asdescriptions of features that may be specific to particular embodimentsof particular inventions. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially be claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various system modulesand components in the embodiments described above should not beunderstood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. As one example, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain some cases, multitasking and parallel processing maybe advantageous.

What is claimed is:
 1. A method comprising: providing tractography datafor presentation to a user, the tractography data characterizing, foreach of a plurality of pairs of parcellations comprising a firstparcellation and a second parcellation determined from a set ofparcellations, a number of neural tracts connecting the firstparcellation and the second parcellation in a brain of a patient;identifying one or more pairs of parcellations from the plurality ofpairs of parcellations for which the number of neural tracts specifiedin the tractography data is outside a normal range of numbers of neuraltracts corresponding to the pair of parcellations, wherein the normalrange of numbers of neural tracts corresponding to a particular pair ofparcellations is defined according to i) an average number of neuraltracts connecting the particular pair of parcellations and ii) a degreeof variation of the number of neural tracts connecting the particularpair of parcellations, and wherein the average number of neural tractsand the degree of variation of the number of neural tracts have beendetermined using brain data corresponding to a plurality of secondpatients; providing data characterizing the one or more identified pairsof parcellations for presentation to a user; determining one or moreelements of interest in the tractography data; determining one or moreparcellations associated with respective elements of interest in thetractography data; obtaining brain atlas data; determining a subset ofthe brain atlas data associated with the determined one or moreparcellations; and providing the subset of the brain atlas data forpresentation to the user.
 2. The method of claim 1, wherein thetractography data specifies a matrix having a plurality of positions,wherein each position in the matrix characterizes the number of neuraltracts connecting a respective pair of parcellations.
 3. The method ofclaim 2, wherein determining one or more elements of interest in thematrix comprises receiving, from the user, a selection of one or morepositions in the matrix.
 4. The method of claim 3, wherein receiving aselection of one or more positions in the matrix comprises receiving oneor more of: a selection of a column of the matrix or a selection of arow of the matrix.
 5. The method of claim 1, further comprising:automatically generating, from the subset of the brain atlas data,target data for a treatment of the patient that targets the determinedone or more parcellations; and providing the target data for processingby one or more medical devices for providing the treatment to thepatient.
 6. The method of claim 5, further comprising: executing, by theone or more medical devices and using the target data, the treatment ofthe patient.
 7. The method of claim 5, wherein the target data comprisesone or more of: data for operating an image guidance system; data foroperating a transcranial magnetic stimulation system; or data foroperating a guidance system during targeted surgery.
 8. A systemcomprising one or more computers and one or more storage devices storinginstructions that are operable, when executed by the one or morecomputers, to cause the one or more computers to perform operationscomprising: providing tractography data for presentation to a user, thetractography data characterizing, for each of a plurality of pairs ofparcellations comprising a first parcellation and a second parcellationdetermined from a set of parcellations, a number of neural tractsconnecting the first parcellation and the second parcellation in thebrain of a patient; identifying one or more pairs of parcellations fromthe plurality of pairs of parcellations for which the number of neuraltracts specified in the tractography data is outside a normal range ofnumbers of neural tracts corresponding to the pair of parcellations,wherein the normal range of numbers of neural tracts corresponding to aparticular pair of parcellations is defined according to i) an averagenumber of neural tracts connecting the particular pair of parcellationsand ii) a degree of variation of the number of neural tracts connectingthe particular pair of parcellations, and wherein the average number ofneural tracts and the degree of variation of the number of neural tractshave been determined using brain data corresponding to a plurality ofsecond patients; providing data characterizing the one or moreidentified pairs of parcellations for presentation to a user;determining one or more elements of interest in the tractography data;determining one or more parcellations associated with respectiveelements of interest in the tractography data; obtaining brain atlasdata; determining a subset of the brain atlas data associated with thedetermined one or more parcellations; and providing the subset of thebrain atlas data for presentation to the user.
 9. The system of claim 8,wherein the tractography data specifies a matrix having a plurality ofpositions, wherein each position in the matrix characterizes the numberof neural tracts connecting a respective pair of parcellations.
 10. Thesystem of claim 9, wherein determining one or more elements of interestin the matrix comprises receiving, from the user, a selection of one ormore positions in the matrix.
 11. The system of claim 10, whereinreceiving a selection of one or more positions in the matrix comprisesreceiving one or more of: a selection of a column of the matrix or aselection of a row of the matrix.
 12. The system of claim 8, theoperations further comprising: automatically generating, from the subsetof the brain atlas data, target data for a treatment of the patient thattargets the determined one or more parcellations; and providing thetarget data for processing by one or more medical devices for providingthe treatment to the patient.
 13. The system of claim 12, wherein thetarget data comprises one or more of: data for operating an imageguidance system; data for operating a transcranial magnetic stimulationsystem; or data for operating a guidance system during targeted surgery.14. One or more non-transitory storage media storing instructions thatwhen executed by one or more computers cause the one or more computersto perform operations comprising: providing tractography data forpresentation to a user, the tractography data characterizing, for eachof a plurality of pairs of parcellations comprising a first parcellationand a second parcellation determined from a set of parcellations, anumber of neural tracts connecting the first parcellation and the secondparcellation in a brain of a patient; identifying one or more pairs ofparcellations from the plurality of pairs of parcellations for which thenumber of neural tracts specified in the tractography data is outside anormal range of numbers of neural tracts corresponding to the pair ofparcellations, wherein the normal range of numbers of neural tractscorresponding to a particular pair of parcellations is defined accordingto i) an average number of neural tracts connecting the particular pairof parcellations and ii) a degree of variation of the number of neuraltracts connecting the particular pair of parcellations, and wherein theaverage number of neural tracts and the degree of variation of thenumber of neural tracts have been determined using brain datacorresponding to a plurality of second patients; providing datacharacterizing the one or more identified pairs of parcellations forpresentation to a user; determining one or more elements of interest inthe tractography data; determining one or more parcellations associatedwith respective elements of interest in the tractography data; obtainingbrain atlas data; determining a subset of the brain atlas dataassociated with the determined one or more parcellations; and providingthe subset of the brain atlas data for presentation to the user.
 15. Thenon-transitory storage media of claim 14, wherein the tractography dataspecifies a matrix having a plurality of positions, wherein eachposition in the matrix characterizes the number of neural tractsconnecting a respective pair of parcellations.
 16. The non-transitorystorage media of claim 15, wherein determining one or more elements ofinterest in the matrix comprises receiving, from the user, a selectionof one or more positions in the matrix.
 17. The non-transitory storagemedia of claim 16, wherein receiving a selection of one or morepositions in the matrix comprises receiving one or more of: a selectionof a column of the matrix or a selection of a row of the matrix.
 18. Thenon-transitory storage media of claim 14, the operations furthercomprising: automatically generating, from the subset of the brain atlasdata, target data for a treatment of the patient that targets thedetermined one or more parcellations; and providing the target data forprocessing by one or more medical devices for providing the treatment tothe patient.
 19. The non-transitory storage media of claim 18, whereinthe target data comprises one or more of: data for operating an imageguidance system; data for operating a transcranial magnetic stimulationsystem; or data for operating a guidance system during targeted surgery.